# -*- coding: utf-8 -*-
"""
Created on Wed Jun 26 16:49:20 2019

@author: Lee
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['label'] = iris.target
df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']

plt.scatter(df[:50]['sepal length'], df[:50]['sepal width'], label='0')
plt.scatter(df[50:100]['sepal length'], df[50:100]['sepal width'], label='1')
plt.xlabel('sepal length')
plt.ylabel('sepal width')
plt.legend()

data = np.array(df.iloc[:100, [0, 1, -1]])
X, y = data[:,:-1], data[:,-1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)


# 根据SMO序列最小最优化算法更新参数值
class SVM:
    def __init__(self, max_iter=100, kernel='linear'):
        self.max_iter = max_iter
        self._kernel = kernel
    
    def init_args(self, features, labels):
        self.m, self.n = features.shape
        self.X = features
        self.Y = labels
        self.b = 0.0
        
        # 将Ei保存在一个列表里
        self.alpha = np.ones(self.m)
        self.E = [self._E(i) for i in range(self.m)]
        # 惩罚参数C
        self.C = 1.0
        
    # 判断是否满足KKT条件    
    def _KKT(self, i):
        y_g = self._g(i)*self.Y[i]                            
        if self.alpha[i] == 0:                           #7.111
            return y_g >= 1
        elif 0 < self.alpha[i] < self.C:                 #7.112 间隔边界
            return y_g == 1
        else:                                            #7.113
            return y_g <= 1
    
    # g(x)预测值，输入xi（X[i]）
    def _g(self, i):                                     #7.104
        r = self.b
        for j in range(self.m):
            r += self.alpha[j]*self.Y[j]*self.kernel(self.X[i], self.X[j])
        return r
    
    # 核函数
    def kernel(self, x1, x2):
        if self._kernel == 'linear':
            return sum([x1[k]*x2[k] for k in range(self.n)])
        elif self._kernel == 'poly':
            return (sum([x1[k]*x2[k] for k in range(self.n)]) + 1)**2
        else:
            return 0
    
    # E（x）为g(x)对输入x的预测值和y的差
    def _E(self, i):                                     #7.105
        return self._g(i) - self.Y[i]
    
    def _init_alpha(self):
        # 外层循环首先遍历所有满足0<a<C的样本点，检验是否满足KKT
        index_list = [i for i in range(self.m) if 0 < self.alpha[i] < self.C]
        # 否则遍历整个训练集
        non_satisfy_list = [i for i in range(self.m) if i not in index_list]
        index_list.extend(non_satisfy_list)
        
        for i in index_list:
            if self._KKT(i):
                continue
            
            E1 = self.E[i]
            # 第二个变量的选择的标准是希望能使alpha2有足够大的变化，选择使E1-E2的绝对值最大的变量alpha2
            # 如果E1是正的，选择最小的；如果E1是负的，选择最大的
            if E1 >= 0:
                j = min(range(self.m), key=lambda x: self.E[x])
            else:
                j = max(range(self.m), key=lambda x: self.E[x])
            return i, j
        
    # 剪辑使满足0<=alpha<=C的约束条件   
    def _compare(self, _alpha, L, H):         #7.108
        if _alpha > H:
            return H
        elif _alpha < L:
            return L
        else:
            return _alpha      
    
    def fit(self, features, labels):
        self.init_args(features, labels)
        
        for t in range(self.max_iter):
            # train
            i1, i2 = self._init_alpha()
            
            # 边界
            if self.Y[i1] == self.Y[i2]:                                   
                L = max(0, self.alpha[i1]+self.alpha[i2]-self.C)
                H = min(self.C, self.alpha[i1]+self.alpha[i2])
            else:
                L = max(0, self.alpha[i2]-self.alpha[i1])
                H = min(self.C, self.C+self.alpha[i2]-self.alpha[i1])
                
            E1 = self.E[i1]
            E2 = self.E[i2]
            # eta=K11+K22-2K12
            eta = self.kernel(self.X[i1], self.X[i1]) + self.kernel(self.X[i2], self.X[i2]) - 2*self.kernel(self.X[i1], self.X[i2])            #7.107
            if eta <= 0:
                # print('eta <= 0')
                continue
                
            alpha2_new_unc = self.alpha[i2] + self.Y[i2] * (E1 - E2) / eta    #7.106
            alpha2_new = self._compare(alpha2_new_unc, L, H)                  #7.109
            
            alpha1_new = self.alpha[i1] + self.Y[i1] * self.Y[i2] * (self.alpha[i2] - alpha2_new)
            
            b1_new = -E1 - self.Y[i1] * self.kernel(self.X[i1], self.X[i1]) * (alpha1_new-self.alpha[i1]) - self.Y[i2] * self.kernel(self.X[i2], self.X[i1]) * (alpha2_new-self.alpha[i2])+ self.b    #7.115
            b2_new = -E2 - self.Y[i1] * self.kernel(self.X[i1], self.X[i2]) * (alpha1_new-self.alpha[i1]) - self.Y[i2] * self.kernel(self.X[i2], self.X[i2]) * (alpha2_new-self.alpha[i2])+ self.b    #7.116
            
            if 0 < alpha1_new < self.C and 0 < alpha2_new < self.C:
                b_new = b1_new
            else:
                b_new = (b1_new + b2_new) / 2
 
            # 更新参数
            self.alpha[i1] = alpha1_new
            self.alpha[i2] = alpha2_new
            self.b = b_new
            
            self.E[i1] = self._E(i1)
            self.E[i2] = self._E(i2)
        print('train done!')

            
    def predict(self, data):
        r = self.b
        for i in range(self.m):
            r += self.alpha[i] * self.Y[i] * self.kernel(data, self.X[i])
            
        return 1 if r > 0 else -1
    
    def score(self, X_test, y_test):
        right_count = 0
        for i in range(len(X_test)):
            result = self.predict(X_test[i])
            if result == y_test[i]:
                right_count += 1
        return right_count / len(X_test)

    
svm = SVM(max_iter=100)
svm.fit(X_train, y_train)
print(svm.score(X_test, y_test))

#sklearn

from sklearn.svm import SVC
clf = SVC()
clf.fit(X_train, y_train)

print(clf.score(X_test, y_test))